Google's ability to recognize the meaning and intent behind user queries rather than merely matching keywords is based on which of the following?

Question

BlackTom AI · Accepted Answer

The question asks about the concept behind Google's ability to recognize meaning and intent behind user queries, not just rely on keywords.
First, consider what would typically be proposed as alternatives: keyword matching (where the system looks for exact words or phrases), pattern matching or rule-based approaches (which rely on hand-crafted rules), and perhaps shallow statistical methods. These approaches focus on surface forms or rigid patterns and struggle to grasp deeper meaning, context, or user intent.
Next, examine why machine learning provides a better explanation. Machine learning enables systems to learn from large-scale data, capture statistical regularities, and infer latent structures such as topics, intents, and semantic relationships between words. In natural language processing, models trained with machine learning can map words to continuous representations (embeddings), capture context with surrounding words, and use architectures like neural networks to predict user intent, disambiguate meaning, and handle synonymy and polysemy.
Moreover, modern search relies on deep learning and transformer-based models that excel at understanding context, semantics, and intent beyond surface keyword matching. These models are trained on vast corpora, optimize for relevance, and can generalize to unseen queries, which is precisely what allows Google to interpret a query's meaning rather than just the literal keywords.
In contrast, keyword-only or rule-based approaches would fail to generalize across varied phrasing, miss nuances, and perform poorly on queries that require inference from context, synonyms, or implied intent. Therefore, the emphasis on recognizing meaning and intent aligns with machine learning-based methods, particularly advances in NLP.
Finally, even if one considers alternatives like static semantic rules or heuristic methods, they do not match the scalability, adaptability, and predictive power of machine learning when it comes to understanding language in a broad, dynamic search environment.

ISA201_D_202610 Final Exam Spring 2025

Google's ability to recognize the meaning and intent behind user queries rather than merely matching keywords is based on which of the following?

查看解析

登录即可查看完整答案

类似问题

TB MC Qu. 08-32 What is the term for the ability... What is the term for the ability of a computer (or machine) to automatically learn on its own without being explicitly programmed to do so?

Which of the following statements is correct?

Artificial Intelligence (AI) uses algorithms to analyse huge streams of data to uncover trends and patterns for improving manufacturing operation, product quality and/or reliability. Which of the following is/are NOT true?

The AI revolution will require continuous retraining and adaptation.

Which of the following is not an essential stage in the machine learning lifecycle?

Which of the following statements BEST describes Machine Learning (ML)?

Match the following models with their general descriptions. 1: k-NN (k-Nearest Neighbors) 2: GMM (Gaussian Mixture Model) 3: Random Forest 4: Logistic Regression 5: Linear Regression 6: k-Means

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单